Most research task-oriented dialog models nowadays focus on end-to-end modeling, i.e., the whole dialog system is integrated into a single neural network [1]}, [2]}. Although recent end-to-end generative approaches based on pre-trained language models produce fluent and natural responses, they suffer from two major problems: (1) hallucinations and lack of grounding [3]}, which result in faulty dialog management or responses inconsistent with the dialog state or database results, and (2) blandness and low lexical diversity of outputs [4]}. On the other hand, retrieval-based dialog systems [5]} select the most appropriate response candidate from a human-generated training set, thus producing varied outputs. However, their responses might not fit the context and can lead to disfluent conversations, especially when the set of candidates is sparse. This limits their usage to very large datasets which do not support dialog state tracking or database access [6]}, [7]}.
